Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/12932
DC FieldValueLanguage
dc.contributor.authorMellit, Adel-
dc.contributor.authorKalogirou, Soteris A.-
dc.date.accessioned2018-11-23T12:43:24Z-
dc.date.available2018-11-23T12:43:24Z-
dc.date.issued2017-09-12-
dc.identifier.citationMcEvoy's handbook of photovoltaics : fundamentals and applications, 2017, Pages 735-761en_US
dc.identifier.isbn9780128103975-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/12932-
dc.description.abstractThis chapter presents four of the major artificial intelligence (AI) techniques for photovoltaic applications: artificial neural networks (ANNs), fuzzy logic (FL), genetic algorithm (GA), and hybrid systems (HSs). The advantages of AI-based modeling and simulation techniques as alternatives to conventional physical modeling are explained.The text validates the premise that AI offers alternative ways to improve prediction accuracy and fault identification. The importance of digital hardware modules that can be integrated within systems is emphasized.Applications of AI techniques for modeling, control, sizing, prediction, and fault detection are described in some detail; conclusions are presented for each of the main AI techniques. References are provided for information on setup techniques.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© 2018 Elsevier Ltden_US
dc.subjectAI techniquesen_US
dc.subjectArtificial neural networksen_US
dc.subjectFuzzy logicen_US
dc.subjectGenetic algorithmen_US
dc.subjectHybrid systemsen_US
dc.titleA survey on the application of artificial intelligence techniques for photovoltaic systemsen_US
dc.typeBook Chapteren_US
dc.doihttps://doi.org/10.1016/B978-0-12-809921-6.00019-7en_US
dc.collaborationJijel Universityen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.countryAlgeriaen_US
dc.countryCyprusen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
cut.common.academicyear2017-2018en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_3248-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.openairetypebookPart-
crisitem.author.deptDepartment of Mechanical Engineering and Materials Science and Engineering-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4497-0602-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Κεφάλαια βιβλίων/Book chapters
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